PlanetScale vs Neon: Serverless Postgres and MySQL Compared

The Actual Problem You're Trying to Solve

You're building a Next.js app — probably with the App Router, probably deployed on Vercel — and you need a database. You don't want to manage a Postgres or MySQL server. You've heard about PlanetScale and Neon, both call themselves 'serverless,' and now you're stuck comparing two things that sound pretty similar on paper but behave completely differently in production.

Serverless databases have been the dominant pitch since around 2022. The idea: you connect over HTTP, pay per query or storage, and stop worrying about provisioning. PlanetScale bet on MySQL with a Git-like branching workflow. Neon bet on Postgres with a copy-on-write architecture and instant branching at the storage level. Different bets, different tradeoffs.

This isn't a benchmark blog post with synthetic numbers. It's a real breakdown of how these two databases feel to actually use — schema management, cold starts, connection handling in serverless functions, and what happens when your free tier runs out.

PlanetScale: MySQL With Branching Built In

PlanetScale is MySQL 8.0 under the hood, running on Vitess — the same sharding layer YouTube built internally. You don't query it over a regular TCP connection from a serverless function. Instead, you use their HTTP-based driver (@planetscale/database), which sidesteps the connection pool problem entirely. Worth noting: this was a genuinely clever move. Lambda functions and Vercel edge functions can't hold open TCP connections between invocations, so a native HTTP query layer is the right architecture.

The signature PlanetScale feature is schema branching. You create a 'branch' of your database (think git checkout -b feature/add-users), make schema changes there, then open a 'deploy request' to merge those changes into production with zero downtime. No ALTER TABLE locking production. It's a real workflow improvement, not a marketing gimmick — especially if you've ever deployed a migration that locked a busy table for 45 seconds.

That said, PlanetScale dropped their free tier in March 2024, which stung a lot of indie developers. The cheapest plan now sits at $39/month for Scaler, which is fine for a production app but painful for side projects. They also don't support foreign key constraints by default (Vitess limitation) — you can enable them, but it adds overhead and their official recommendation is still to handle referential integrity in application code. If you're coming from a strict relational background, that's going to feel wrong.

One more thing — PlanetScale's connection limits are generous because you're just making HTTP calls. There's no 'max_connections' wall to hit. For Next.js API routes or edge functions that spin up hundreds of concurrent instances, that matters more than you'd think.

Neon: Postgres With a Copy-on-Write Storage Engine

Neon is real Postgres — currently tracking Postgres 16 — with a custom storage layer they built from scratch. The storage separates compute from data. Your 'compute' is a Postgres process that can spin up and spin down; storage persists independently on their distributed layer. This means Neon can scale your compute to zero between requests (the free tier does this aggressively) and spin it back up on demand.

Cold starts are the honest downside. Spinning up a compute from zero takes anywhere from 100ms to 500ms in practice. On the free tier it's often closer to the higher end. Paid tiers let you configure a 'min compute size' so you're never fully at zero, but then you're paying for always-on compute which starts to look more like a traditional DB. Neon calls this 'autoscaling' — you set a min and max vCPU, and they scale between them based on load. In practice for most small apps, you set min to 0.25 vCPU and accept the occasional slow cold start.

Branching in Neon works differently than PlanetScale. Instead of schema-only branching, you branch the entire database — data included. Neon creates the branch in milliseconds using copy-on-write, so you get a full clone of your prod data (up to some size limit) instantly. This is extremely useful for testing migrations against real data shapes without seeding a test database. The branching model here is genuinely more flexible than PlanetScale's schema-only approach.

The connection story is the trickiest part of Neon for serverless. Standard Postgres uses TCP connections, which is a problem in serverless environments where you can have hundreds of short-lived function instances. Neon ships a connection pooler (@neondatabase/serverless with WebSocket or HTTP mode) specifically for this. You should absolutely use it — without it, you'll hit connection limit errors under any real traffic. Quick aside: if you're using Drizzle or Prisma, both have documented Neon serverless setups, and they're not complicated to wire up.

Neon's free tier is actually generous: 0.5 GB storage, 190 compute-hours per month. For a side project that's not under constant traffic, it holds up. The paid 'Launch' plan at $19/month gives you 10 GB storage and 300 compute-hours, which covers most production apps that aren't big.

Schema Management and Migrations

Honestly, schema management is where the two platforms feel most different day-to-day. PlanetScale's deploy requests are opinionated — you work through their UI or CLI, you open a deploy request, and the migration runs in the background without blocking reads or writes. It handles the scary parts of ALTER TABLE for you. If you're using Prisma, you run prisma db push on a branch, review the diff in the PlanetScale console, and deploy. It's a nicer workflow than most teams have with raw SQL migrations.

Neon doesn't have its own migration workflow — it's just Postgres, so you bring your own tooling. Prisma Migrate, Drizzle's migration runner, Flyway, raw SQL files with a deploy hook — all fine. The branching feature helps here: you run your migration on a Neon branch, verify it, then run it against production. But you're still responsible for making that migration safe (using CREATE INDEX CONCURRENTLY instead of the blocking version, etc.). There's no deploy-request safety net.

For teams that are already disciplined about migrations, Neon's approach is actually less friction — you're not adopting a new workflow on top of your existing tooling. For teams that have burned themselves with bad migrations in the past, PlanetScale's guardrails are worth something real. It depends on your team, not on which platform is objectively better.

Performance and Connection Handling in Next.js

Running either of these in a Next.js app with the App Router in 2026 is well-documented. Both platforms have official Next.js quickstarts. That said, there are a few things the quickstarts don't tell you.

With PlanetScale, you instantiate the client at module scope — it's just an HTTP client, so there's no connection to manage. One file, done: ``ts import { connect } from '@planetscale/database'; export const db = connect({ host: process.env.DATABASE_HOST, username: process.env.DATABASE_USERNAME, password: process.env.DATABASE_PASSWORD, }); ` With Neon in serverless mode, you want to use their WebSocket or HTTP driver and avoid creating a new client on every request: `ts import { neon } from '@neondatabase/serverless'; const sql = neon(process.env.DATABASE_URL!); export async function getUser(id: string) { const [user] = await sqlSELECT * FROM users WHERE id = ${id}; return user; } `` If you're using an ORM like Drizzle, the Neon adapter handles this for you — you just pass the neon function as the driver. Look, both setups are fine. Neither will cause you problems unless you accidentally put client initialization inside a request handler.

Latency-wise, if your Vercel deployment is in us-east-1 and your database is also in us-east-1, you're typically looking at 10-30ms query latency on warm paths for both platforms. The Neon cold start adds that 100-500ms hit on top. PlanetScale's HTTP driver adds a small overhead compared to raw TCP, but it's consistent — no cold start spikes. For user-facing APIs where tail latency matters, PlanetScale's consistency is easier to reason about.

Pricing: What You Actually Pay

PlanetScale's cheapest paid plan is $39/month (Scaler). That gets you 10 GB storage, 1 billion row reads per month, and 10 million row writes. For a production app with moderate traffic, you probably won't hit those limits. But $39/month for a side project or a client app that doesn't yet have revenue is real money.

Neon's free tier is real and usable. The $19/month Launch plan is reasonable. If you need more compute (autoscaling above 2 vCPUs) or more storage (above 10 GB), you move to the Scale plan at $69/month. The per-compute-hour billing on Neon can get unpredictable under bursty traffic — something to watch. PlanetScale's row-read billing is easier to forecast if you know your query patterns.

Worth noting: both platforms charge separately for data transfer in some configurations. Check the current pricing pages directly before committing — these numbers shift more often than most people realize, and what's accurate today might not be accurate in six months.

If cost is your main constraint: Neon for free and low-cost, PlanetScale if you have a production app with budget and want the schema branching workflow. That's the honest split.

Which One Should You Actually Use?

If you're building with Postgres already (using Prisma with Postgres, or Drizzle with Postgres), Neon is the obvious choice. It's real Postgres, your schema and queries just work, and the branching is genuinely useful. The cold start issue is real but manageable — configure a 0.25 vCPU minimum on the Launch plan and you'll rarely notice it.

If you're building a new app, have a team that's used MySQL, or you want guardrails around schema changes and are willing to pay $39/month, PlanetScale's workflow is legitimately good. The deploy-request model for migrations is the best version of 'safe schema changes' that exists as a managed service right now.

In practice, the Postgres ecosystem in 2026 is wider. More ORMs, more extensions, more community tooling, better support in new frameworks. If you're picking from scratch with no prior preference, Postgres wins on ecosystem breadth and Neon is a solid way to run it serverlessly. If you're pairing this with a full Next.js App Router setup, check out nextjs-server-actions and nextjs-caching-strategies — the database choice interacts with how you handle caching and server mutations more than most tutorials acknowledge.

On the UI side — if you're building dashboards or data-heavy interfaces on top of either database, Empire UI has components that work well for tables, filters, and data-display patterns. Worth browsing if you're also trying to ship a polished frontend alongside your backend infrastructure choices.

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